The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of the large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
One example of an anatomical structure that is often studied in medical images is the spine. Magnetic resonance imaging (MRI) is often used for spine imaging due to the high contrast between soft tissues. Digital images of the spine may be constructed by using raw image data obtained from an MRI scanner. Such digital images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”).
In some spine imaging applications, 3-D scout scans are used to improve MR spinal analysis workflow, since they can provide isotropic resolutions with large fields of view covering the entire spine. Such 3-D scout scans are typically manually examined by technicians so as to label the vertebrae and identify imaging planes that are parallel to the inter-vertebral discs. Given the large amount of image data generated by any given image scan, however, such manual investigation can be tedious and prone to inaccuracy. It is therefore desirable to provide an automatic technique that detects anatomical features in the selected regions of an image for further diagnosis of any disease or condition, and to improve MR spine workflow.